New energy charging pile power distribution method and system for multiple charging guns

Abstract

The application relates to the technical field of charging pile power distribution, in particular to a new energy charging pile power distribution method and system for multiple charging guns, which specifically comprises: time-based priority weight distribution, which follows the first-come-first-served principle, so that users who arrive first can enjoy higher charging power in priority; a demand representation factor of an electric vehicle is calculated according to the residual power of the electric vehicle, so that the electric vehicle with low power and urgent need for charging can obtain certain electric energy first, thereby improving the charging efficiency; the time expectation distribution weight of each charging pile is determined based on the power change during charging; an inhibition factor is introduced based on the total output power change trend prediction result to cope with the total output power change in advance; the distribution power of the charging pile is adjusted in real time through comprehensive analysis; the user waiting time is reduced through dynamic distribution, and efficient utilization of the total output power, improvement of user experience and optimization of charging station operation management are realized.

Description

Technical Field

[0001] This application relates to the field of charging pile power allocation technology, specifically to a power allocation method and system for new energy charging piles with multiple charging guns. Background Technology

[0002] With the acceleration of the global energy transition, new energy vehicles have ushered in unprecedented development opportunities, and their market share has experienced explosive growth. As a key infrastructure for new energy vehicles, charging stations are becoming increasingly important. However, existing charging station technologies face numerous challenges in handling the simultaneous charging needs of multiple electric vehicles, particularly in power distribution. More efficient and intelligent solutions are urgently needed to improve charging efficiency and user experience.

[0003] Traditional charging stations mostly use a single-gun design. Even among the few multi-gun charging stations, their power distribution is relatively simple, usually either average or a fixed ratio. This distribution method cannot dynamically adjust according to the actual charging needs of the electric vehicle and the battery status, resulting in low charging efficiency and failing to fully utilize the charging station's performance. For example, during peak charging periods, when multiple vehicles are charging simultaneously, due to unreasonable power distribution, some electric vehicles may experience excessively slow charging speeds, or even fail to meet basic charging needs, severely impacting the user's charging experience and acceptance of new energy vehicles. Summary of the Invention

[0004] To address the aforementioned technical problems, the purpose of this application is to provide a power allocation method and system for new energy charging piles with multiple charging guns. The specific technical solution adopted is as follows:

[0005] In a first aspect, embodiments of this application provide a power allocation method for new energy charging piles with multiple charging guns, the method comprising the following steps:

[0006] The system collects in real time the total output power of the power distribution cabinet of the charging station and the output power allocated by the power distribution cabinet to each charging pile, as well as the actual remaining power of each charging vehicle connected to each charging pile.

[0007] At the current moment, the priority weight of each charging pile is set based on the usage time of each charging pile; the electric vehicle demand characterization factor of each charging pile is calculated based on the actual remaining power of all electric vehicles connected to each charging pile; based on the difference in output power of the charging pile corresponding to the start charging time of each electric vehicle and the current moment, the change in the expected charging time of each electric vehicle is analyzed, and the time expectation allocation weight of each charging pile is determined.

[0008] The total output power of the distribution cabinet at multiple future times is predicted by the total output power of the distribution cabinet at historical times; based on the differences between all predicted total output power values ​​and the total output power of the distribution cabinet at the current time, as well as the degree of disorder in the distribution of all predicted total output power values, the suppression factor of all charging piles at the current time is calculated.

[0009] The power allocation for each charging pile at the current moment is determined based on the total output power of the distribution cabinet at the current moment, the priority weight, the trolley demand characterization factor, the suppression factor, and the time expectation allocation weight; if the current moment is a preset adjustment moment, the output power of each charging pile is allocated through the power allocation.

[0010] In one embodiment, the actual remaining power is the product of the remaining percentage of power of the charging vehicle and the total capacity of the charging vehicle's battery.

[0011] In one embodiment, the process of obtaining the priority weight is as follows:

[0012] The priority weight is set to M+1 levels from 0 to M, where M equals the number of charging piles. For the charging piles currently in use, the current continuous usage time of each charging pile is obtained, and all charging piles are sorted from largest to smallest according to their current continuous usage time. The priority weight of the charging pile with the longest current continuous usage time is set as the highest priority weight M, and the priority weights of each charging pile are assigned in descending order according to the sorting order. The priority weight of the charging piles that are not currently in use is set to 0.

[0013] In one embodiment, the process of obtaining the tram demand characterization factor is as follows:

[0014] Calculate the normalized mean of the actual remaining power of all electric vehicles connected to all charging guns of each charging station, and use the difference between the natural number 1 and the mean as the electric vehicle demand characterization factor for each charging station.

[0015] In one embodiment, the expression for the time expectation allocation weight is:

[0016]

[0017] Where Y represents the expected time allocation weight of the current charging station at the current moment; n is the number of charging guns contained in the current charging station; Pv v Ms represents the charging time of the electric vehicle connected to the v-th charging gun of the current charging station at the current moment; v This represents the estimated time required for the vehicle connected to the v-th charging gun of the current charging station to be fully charged at the current moment; Tb vThis indicates the estimated time required for the vehicle connected to the v-th charging gun of the current charging station to be fully charged when it just starts charging.

[0018] In one embodiment, the expression for the inhibition factor is:

[0019]

[0020] Where U represents the suppression factor of all charging piles at the current time; μ represents the average of the differences between the total output power prediction values ​​of all future times and the total output power of the distribution cabinet at the current time; δ represents the variance of the total output power prediction values ​​of all future times; norm() is the normalization function.

[0021] In one embodiment, the process of obtaining the allocated power is as follows:

[0022] Calculate the final allocation weight Mb of the i-th charging pile at the current time. i Mb i The expression is: Mb i =b+Rc i +Hg i +U×Q i Where b represents the preset initial allocation weight of any charging pile; Rc i Hg is the normalized value of the priority weight of the i-th charging pile at the current moment; i Q represents the normalized value of the electric vehicle demand representation factor for the i-th charging pile at the current time; U represents the suppression factor for all charging piles at the current time; i This represents the normalized value of the expected time allocation weight of the i-th charging pile at the current moment;

[0023] The power allocation for each charging pile at the current moment is determined based on the final allocation weight and the total output power of the distribution cabinet at the current moment.

[0024] In one embodiment, the allocated power of each charging pile at the current moment is the product of the normalized value of the final allocation weight of each charging pile at the current moment and the total output power of the power distribution cabinet.

[0025] In one embodiment, if the current time is a preset adjustment time, the distribution of output power to each charging pile through the power allocation method specifically includes:

[0026] Every preset time interval, the output power of each charging pile is redistributed using the allocated power; when a new electric vehicle comes to charge at any charging pile, the output power of each charging pile is also redistributed.

[0027] Secondly, embodiments of this application also provide a power distribution system for new energy charging piles with multiple charging guns, including a memory, a processor, and a computer program stored in the memory and running on the processor, wherein the processor executes the computer program to implement the steps of any of the methods described above.

[0028] The embodiments of this application have at least the following beneficial effects:

[0029] This application achieves efficient utilization of total output power, meets the personalized charging needs of electric vehicles, improves user experience, and optimizes the operation and management of charging stations through precise dynamic power allocation, providing a more intelligent, efficient, and reliable solution for charging new energy vehicles.

[0030] This application introduces multiple allocation weights to consider various practical needs of electric vehicle charging, enabling adaptive power allocation to avoid the problem of average distribution, improve charging efficiency, and ensure that electric vehicles receive appropriate charging services. Specifically, time-based priority weight allocation follows a first-come, first-served principle, allowing users who arrive first to enjoy higher charging power, aligning with users' daily habits and reducing waiting time. Electric vehicle demand characteristics are calculated based on remaining battery power, prioritizing vehicles with low battery levels and urgent charging needs, improving charging efficiency, ensuring the normal use of new energy vehicles, and reducing the risk of users being unable to travel due to insufficient power. The application determines the time expectation allocation weights for each charging station based on power changes during charging. An inhibition factor is introduced based on the predicted trend of total output power changes to proactively address changes in total output power, enhancing charging stability and preventing charging interruptions or delays caused by fluctuations in the total output power of the distribution cabinet or changes in the status of other charging stations. Dynamic allocation reduces user waiting time, allowing users to accurately predict charging completion time and rationally plan their trips. Predicting changes in total output power and adjusting allocation weights enhances charging stability and reduces the risk of interruptions or delays. Attached Figure Description

[0031] To more clearly illustrate the technical solutions and advantages in the embodiments of this application or the prior art, the drawings used in the description of the embodiments or the prior art will be briefly introduced below. Obviously, the drawings described below are only some embodiments of this application. For those skilled in the art, other drawings can be obtained based on these drawings without creative effort.

[0032] Figure 1 A flowchart illustrating the steps of a power allocation method for new energy charging piles with multiple charging guns, provided in one embodiment of this application.

[0033] Figure 2 This is a schematic diagram illustrating the process of obtaining the characteristics of electric vehicle demand. Detailed Implementation

[0034] To further illustrate the technical means and effects adopted by this application to achieve the intended purpose of the invention, the following, in conjunction with the accompanying drawings and preferred embodiments, details the specific implementation, structure, features, and effects of the power allocation method and system for new energy charging piles with multiple charging guns proposed in this application. In the following description, different "one embodiment" or "another embodiment" do not necessarily refer to the same embodiment. Furthermore, specific features, structures, or characteristics in one or more embodiments can be combined in any suitable form.

[0035] Unless otherwise defined, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this application pertains.

[0036] The following, in conjunction with the accompanying drawings, details the specific scheme of the power allocation method and system for new energy charging piles with multiple charging guns provided in this application.

[0037] Please see Figure 1 The diagram illustrates a flowchart of a power allocation method for new energy charging piles with multiple charging guns according to an embodiment of this application. The method includes the following steps:

[0038] Step S1: Real-time data collection of the total output power of the power distribution cabinet of the charging station, the output power allocated by the power distribution cabinet to each charging pile, and the actual remaining power of each charging vehicle connected to each charging pile.

[0039] The system obtains the number of charging piles in the charging station and monitors the total output power of the power distribution cabinet in the charging station in real time. The total output power is the total amount of charging power that the power distribution cabinet can provide to all charging piles. At the same time, the system obtains the output power that the power distribution cabinet allocates to each charging pile in real time.

[0040] Each charging station contains multiple charging guns, capable of charging multiple electric vehicles simultaneously. For any electric vehicle connected to a charging gun in use, the remaining percentage of battery power is acquired in real time, and combined with the total battery capacity of the electric vehicle, the actual remaining battery power at each moment is determined. For example, assuming the total battery capacity of the electric vehicle is A kWh, and the current remaining percentage is displayed as 30%, then the actual remaining battery power of the electric vehicle at that moment is A * 30% kWh.

[0041] In this embodiment, the data collection frequency for various data types is set to once every ten minutes. In other embodiments of this application, implementers can set the data collection frequency according to their specific circumstances.

[0042] Step S2: At the current moment, set the priority weight of each charging pile based on the usage time of each charging pile; calculate the electric vehicle demand characterization factor of each charging pile based on the actual remaining power of all electric vehicles connected to each charging pile; analyze the change in the expected charging time of each electric vehicle based on the difference in output power of the charging pile corresponding to the start charging time of each electric vehicle and the current moment, and determine the time expectation allocation weight of each charging pile.

[0043] The limitation of the total output power of the power distribution cabinet also limits the output power of the charging piles, causing these charging piles to mutually restrict each other's output power. When the total output power is insufficient, excessive power from one charging pile will limit the power of other charging piles, affecting the charging speed. Therefore, it is necessary to allocate power to each charging pile so that the allocation result meets the usage needs of each charging pile as much as possible.

[0044] (1) First, obtain the maximum output power of each charging pile, calculate the sum of the maximum output power of all charging piles in the charging station at the current moment, and record it as the power sum value P. If the power sum value P is less than or equal to the total output power of the distribution cabinet, it means that each charging pile can use the maximum output power for charging. Conversely, if the power sum value P is greater than the total output power of the distribution cabinet, it is necessary to dynamically allocate according to the needs of each charging pile in order to meet the needs of each charging user as much as possible.

[0045] (2) In the case where the sum of power P is greater than the total output power, this application first sets an initial allocation weight for each charging pile. This allocation weight is used to determine the power allocated by the distribution cabinet to each charging pile. In this application, the initial allocation weight of each charging pile is set to 1, that is, when all charging piles are not in use, all charging piles are allocated the same amount of power. The allocation weight of each charging pile can then be adjusted according to the real-time usage of the charging piles, thereby adaptively adjusting the power allocation result of the charging piles.

[0046] Furthermore, priority weights are assigned to each charging pile based on the order in which they are used. That is, based on the principle of "first come, first served," priority weights are set for the charging power of each charging pile, and the power of the corresponding charging pile can be dynamically allocated according to the priority weights.

[0047] Specifically, assuming the number of charging piles is M, this application sets priority weights to M+1 levels, from 0 to M. Priority weights are dynamically assigned based on the time a charging pile is used or reserved. First, for charging piles currently in use, the current continuous usage time of each charging pile is obtained, wherein at least one charging gun of each charging pile is in use during its current continuous usage time. Then, all charging piles are arranged in descending order of their current continuous usage time. The charging pile with the longest current continuous usage time is assigned the highest priority weight M, and priority weights are assigned to each charging pile in descending order of their arrangement. If a charging pile is not currently in use, its priority weight is set to 0. For example, suppose there are five charging stations: A, B, C, D, and E. A is used for 1 hour, B for 50 minutes, and C for 30 minutes. D and E are not used. Then M = 5. Therefore, the priority weights of A, B, and C are 5, 4, and 3 respectively, while the priority weights of D and E are both 0.

[0048] The priority weight of each charging pile at the current moment is normalized. The normalization method adopted in this application is as follows: Where Rc i R represents the normalized priority weight value of the i-th charging pile at the current moment. i R j Let represent the priority weights of the i-th and j-th charging piles at the current moment, respectively, and M represent the number of charging piles.

[0049] (3) Then, since some electric vehicles have low battery power, they may need to be charged urgently instead of being fully charged before leaving. Therefore, the charging can be allocated according to the remaining battery power of the electric vehicles corresponding to the charging pile, and the electric vehicles with low battery power should be charged first.

[0050] Based on the above analysis, the electric vehicle demand characterization factor for each charging station at the current moment is calculated, and the expression is:

[0051]

[0052] Among them, H i Let n represent the electric vehicle demand characteristic factor for the i-th charging pile at the current moment. i L represents the number of charging guns contained in the i-th charging station. i,u This represents the normalized value of the actual remaining power of the electric vehicle connected to the u-th charging gun of the i-th charging pile at the current moment.

[0053] In this embodiment, the normalization method for the actual remaining power is as follows: obtain the maximum value of the total battery capacity of all charging vehicles connected to all charging piles at the current moment, and use the ratio of the actual remaining power of each charging vehicle to the maximum value as the normalized value of the actual remaining power of that charging vehicle. Implementers may also use other methods to normalize the actual remaining power, and this application does not impose specific limitations.

[0054] The lower the actual remaining battery power of the electric vehicle charging at the corresponding charging gun of the charging pile, the greater the electric vehicle demand characteristic factor of the charging pile.

[0055] Furthermore, the maximum-minimum method is used to normalize the electric vehicle demand characterization factors of all charging piles at the current moment. Implementers may also use other normalization methods to normalize the electric vehicle demand characterization factors of charging piles, and this application does not impose specific restrictions.

[0056] (4) Analyze the relationship between each preset charging time and the actual charging time, and analyze the completion results based on the current allocation results, so as to dynamically allocate the charging time so that each user can complete the charging within the expected time, reducing user waiting time and improving user experience.

[0057] To allow users to clearly understand the charging time of electric vehicles and thus allocate their time more conveniently, most existing electric vehicles display the estimated charging time during charging. However, due to changes in load and the operating status of other charging stations, the charging power allocated to the charging gun at that charging station can vary. Therefore, to reduce the actual charging time exceeding the initial preset charging time caused by these changes, it is necessary to calculate the expected time allocation weight for each charging station at the current moment based on the relationship between the sum of the current charging time and the current estimated charging time, and the initial preset charging time. This will avoid or reduce the actual time exceeding the preset time. The expression for the expected time allocation weight is as follows:

[0058]

[0059] Where Y represents the expected time allocation weight of the current charging station at the current moment; n is the number of charging guns contained in the current charging station; Pv v Ms represents the charging time of the electric vehicle connected to the v-th charging gun of the current charging station at the current moment; v This represents the estimated time required for the electric vehicle connected to the v-th charging gun of the current charging station to be fully charged at the current moment, denoted as the current estimated charging time. Specifically, it is the estimated charging time determined based on the output power of the current charging station and the actual electricity demand of the electric vehicle at the current moment; Tb vThis indicates the estimated time required for the charging vehicle connected to the v-th charging gun of the current charging pile to fully charge at the beginning of charging. It is denoted as the initial estimated charging time. Specifically, it is the estimated charging time determined based on the charging pile power when the user first arrives and uses the charging gun, as well as the actual electricity demand of the charging vehicle at the time of initial arrival.

[0060] The actual power demand of each trolley at the current moment is the difference between the total battery capacity of each trolley at the current moment and the actual remaining power.

[0061] The greater the sum of the current charging time and the preset remaining time at the current moment, the greater the expected time allocation weight for that charging station. Where |Pv v +Ms v |-Tb v If the value is not positive, simply record it as 0.

[0062] The expected time allocation weights of all charging piles at the current moment are normalized. This application normalizes them using the following expression: In the formula, Q i Y is the normalized value that assigns weights to the expected time of the i-th charging pile at the current moment. i Y j The expected time allocation weights for the i-th and j-th charging piles at the current time are respectively, and M is the number of charging piles. Implementers may also use other normalization methods to normalize the expected time allocation weights for all charging piles, and this application does not impose specific restrictions.

[0063] Step S3: Predict the total output power of the distribution cabinet for multiple future times based on the total output power of the distribution cabinet at historical times; calculate the suppression factor of all charging piles at the current time based on the difference between all predicted total output power values ​​and the total output power of the distribution cabinet at the current time, as well as the degree of disorder in the distribution of all predicted total output power values.

[0064] This application predicts the trend of total output power change of the distribution cabinet. If the prediction shows that the total output power is increasing, then under the current situation, the power distribution cabinet can be appropriately biased towards the customers corresponding to the charging piles that are expected to expire or have already expired. Conversely, if the total output power is decreasing, it means that if the distribution is carried out in accordance with the above method, the charging piles that have not yet expired may expire, thus failing to meet the needs of any user.

[0065] Therefore, this application obtains the time expectation allocation weight suppression factor based on the prediction result of the total output power change trend of the distribution cabinet. The specific method is as follows:

[0066] First, the total output power data of the distribution cabinet collected over the past x days prior to the current moment is obtained, and the sequence formed by these data in chronological order is recorded as the historical total output power sequence at the current moment. This total output power sequence is used as input to the existing ARIMA prediction algorithm to predict the total output power at the next 'a' moments, and the variance δ of the predicted total output power at these 'a' moments is calculated, as well as the difference between the predicted total output power at each future moment and the total output power of the distribution cabinet at the current moment. In this embodiment, x is set to 1 and a is set to 6, i.e., predicting the total output power within the next hour. In other embodiments of this application, the implementer can set the values ​​of x and a according to the actual situation. The ARIMA prediction algorithm is a well-known technology, and its specific process will not be described in detail.

[0067] It should be noted that, for the prediction of the total output power sequence, only one prediction method is provided in the embodiments of this application. There are many existing prediction methods, and implementers may also use other prediction methods to predict the total output power sequence. This application does not impose any specific restrictions.

[0068] Then, the total output power prediction results are analyzed to obtain the suppression factor for the time-expected allocation weight of each charging pile at each time point. The expression is as follows:

[0069]

[0070] Where U represents the suppression factor of all charging piles at the current moment; μ represents the average of the differences between the predicted total output power at the next a time and the total output power of the distribution cabinet at the current moment; δ represents the variance of the predicted total output power at the next a time; and norm() is the normalization function. In the embodiments of this application, Where exp() is an exponential function with the natural constant as the base; μ×δ adopts the same expression as... The same method is used for normalization. In other embodiments of this application, implementers may also use other normalization methods for normalization.

[0071] μ reflects the trend of the predicted total output power. A positive trend indicates an increasing trend in total output power, and the smaller the variance of the predicted total output power, the more reliable the trend. Conversely, a negative trend indicates a decreasing trend in total output power, and the predicted total output power shows a large change, which should be suppressed.

[0072] Step S4: Determine the power allocation for each charging pile at the current moment based on the total output power of the distribution cabinet at the current moment, the priority weight, the trolley demand characterization factor, the suppression factor, and the time expectation allocation weight; if the current moment is a preset adjustment moment, allocate the output power of each charging pile through the power allocation.

[0073] The dynamic allocation weight of each charging pile at the current moment is calculated using the following expression:

[0074] Mb i =b+Rc i +Hg i +U×Q i

[0075] Among them, Mb i Rc represents the final allocation weight of the i-th charging pile at the current moment; b represents the preset initial allocation weight of any charging pile; i Hg is the normalized value of the priority weight of the i-th charging pile at the current moment; i Q represents the normalized value of the electric vehicle demand representation factor for the i-th charging pile at the current time; U represents the suppression factor for all charging piles at the current time; i This represents the normalized value of the expected time allocation weight of the i-th charging pile at the current moment.

[0076] The above method is used to analyze each charging pile at the current moment, and the final allocation weight of each charging pile is obtained. The weight is then normalized using the following method: In the formula, Mb i ′ Mb is the normalized value of the allocation weight of the i-th charging pile at the current time. i Mb j , where are the allocation weights of the i-th and j-th charging piles at the current time, and M is the number of charging piles.

[0077] Furthermore, the normalized value of the final allocation weight of each charging pile at the current moment is multiplied by the total output power of the power distribution cabinet to obtain the allocated power of each charging pile at the current moment. The allocated power is less than or equal to its maximum power.

[0078] Using the above method, the power of the charging pile is redistributed every 10 minutes. During this period, whenever a new electric vehicle comes to the charging station to charge, the power of the charging pile is also redistributed after the new electric vehicle connects to the charging gun.

[0079] A schematic diagram illustrating the process of obtaining the characteristics of tram demand is shown below. Figure 2 As shown.

[0080] Based on the same inventive concept as the above methods, this application also provides a power allocation system for new energy charging piles with multiple charging guns, including a memory, a processor, and a computer program stored in the memory and running on the processor. When the processor executes the computer program, it implements the steps of any one of the above-described methods for power allocation of new energy charging piles with multiple charging guns.

[0081] In summary, the embodiments of this application provide a power allocation method for new energy charging piles with multiple charging guns. Through precise dynamic power allocation, it achieves efficient utilization of total output power, meets the personalized charging needs of electric vehicles, improves user experience, and optimizes the operation and management of charging stations, providing a more intelligent, efficient, and reliable solution for charging new energy vehicles.

[0082] This application introduces multiple allocation weights to consider various practical needs of electric vehicle charging, enabling adaptive power allocation to avoid the problem of average distribution, improve charging efficiency, and ensure that electric vehicles receive appropriate charging services. Specifically, time-based priority weight allocation follows a first-come, first-served principle, allowing users who arrive first to enjoy higher charging power, aligning with user habits and reducing waiting time. Electric vehicle demand characteristics are calculated based on remaining battery power, prioritizing vehicles with low battery levels and urgent charging needs, improving charging efficiency, ensuring the normal use of new energy vehicles, and reducing the risk of users being unable to travel due to insufficient power. A suppression factor is introduced based on the predicted trend of total output power changes to proactively address changes in total output power, enhancing charging stability and preventing charging interruptions or delays caused by fluctuations in the total output power of the distribution cabinet or changes in the status of other charging piles. Dynamic allocation reduces user waiting time, allowing users to accurately predict charging completion time and rationally plan their trips. Predicting changes in total output power and adjusting allocation weights enhances charging stability and reduces the risk of interruptions or delays.

[0083] It should be noted that the order of the embodiments described above is merely for descriptive purposes and does not represent the superiority or inferiority of the embodiments. Furthermore, the above description focuses on specific embodiments of this application. Additionally, the processes depicted in the accompanying drawings do not necessarily require a specific or sequential order to achieve the desired results. In some implementations, multitasking and parallel processing are possible or may be advantageous.

[0084] The various embodiments in this application are described in a progressive manner. The same or similar parts between the various embodiments can be referred to each other. Each embodiment focuses on describing the differences from other embodiments.

[0085] The above description is only a preferred embodiment of this application and is not intended to limit this application. Any modifications, equivalent substitutions, improvements, etc., made within the principles of this application should be included within the protection scope of this application.

Claims

1. A power allocation method for new energy charging piles with multiple charging guns, characterized in that, The method includes the following steps: The system collects in real time the total output power of the power distribution cabinet of the charging station and the output power allocated by the power distribution cabinet to each charging pile, as well as the actual remaining power of each charging vehicle connected to each charging pile. At the current moment, the priority weight of each charging pile is set based on the usage time of each charging pile; the electric vehicle demand characterization factor of each charging pile is calculated based on the actual remaining power of all electric vehicles connected to each charging pile; based on the difference in output power of the charging pile corresponding to the start charging time of each electric vehicle and the current moment, the change in the expected charging time of each electric vehicle is analyzed, and the time expectation allocation weight of each charging pile is determined. The total output power of the distribution cabinet at multiple future times is predicted by the total output power of the distribution cabinet at historical times; based on the differences between all predicted total output power values ​​and the total output power of the distribution cabinet at the current time, as well as the degree of disorder in the distribution of all predicted total output power values, the suppression factor of all charging piles at the current time is calculated. The power allocation for each charging pile at the current moment is determined based on the total output power of the distribution cabinet, the priority weight, the trolley demand characterization factor, the suppression factor, and the time expectation allocation weight; if the current moment is a preset adjustment moment, the output power of each charging pile is allocated through the power allocation. The expression for the time-expected allocation weights is: in, This represents the expected weight allocation for the current charging station at the current moment; n is the number of charging guns contained in the current charging station. This indicates the charging time of the electric vehicle connected to the v-th charging gun of the current charging pile at the current moment; This indicates the estimated time required for the vehicle connected to the v-th charging gun of the current charging station to be fully charged at the current moment. This indicates the estimated time required for the charging vehicle connected to the vth charging gun of the current charging pile to be fully charged when it just starts charging. The expression for the inhibitory factor is: Where U represents the suppression factor of all charging piles at the current moment; This represents the average of the differences between the predicted total output power at all future times and the total output power of the distribution cabinet at the current time. This represents the variance of the total predicted output power at all said future times; This is the normalization function.

2. The power allocation method for new energy charging piles with multiple charging guns as described in claim 1, characterized in that, The actual remaining power is the product of the remaining percentage of power of the charging vehicle and the total battery capacity of the charging vehicle.

3. The power allocation method for new energy charging piles with multiple charging guns as described in claim 1, characterized in that, The process for obtaining the priority weight is as follows: The priority weight is set to M+1 levels from 0 to M, where M equals the number of charging piles. For the charging piles currently in use, the current continuous usage time of each charging pile is obtained, and all charging piles are sorted from largest to smallest according to their current continuous usage time. The priority weight of the charging pile with the longest current continuous usage time is set as the highest priority weight M, and the priority weights of each charging pile are assigned in descending order according to the sorting order. The priority weight of the charging piles that are not currently in use is set to 0.

4. The power allocation method for new energy charging piles with multiple charging guns as described in claim 1, characterized in that, The process of obtaining the tram demand characterization factors is as follows: Calculate the normalized mean of the actual remaining power of all electric vehicles connected to all charging guns of each charging station, and use the difference between the natural number 1 and the mean as the electric vehicle demand characterization factor for each charging station.

5. The power allocation method for new energy charging piles with multiple charging guns as described in claim 1, characterized in that, The process of obtaining the allocated power is as follows: Calculate the final allocation weight of the i-th charging pile at the current time. , The expression is: , where b represents the preset initial allocation weight of any charging pile; This is the normalized value of the priority weight of the i-th charging pile at the current moment; U represents the normalized value of the electric vehicle demand characterization factor for the i-th charging pile at the current time; U represents the suppression factor for all charging piles at the current time. This represents the normalized value of the expected time allocation weight of the i-th charging pile at the current moment; The power allocation for each charging pile at the current moment is determined based on the final allocation weight and the total output power of the distribution cabinet at the current moment.

6. The power allocation method for new energy charging piles with multiple charging guns as described in claim 5, characterized in that, The power allocation for each charging pile at the current moment is the product of the normalized value of the final allocation weight of each charging pile at the current moment and the total output power of the distribution cabinet.

7. The power allocation method for new energy charging piles with multiple charging guns as described in claim 1, characterized in that, If the current time is a preset adjustment time, the output power of each charging pile is allocated through the power allocation method, specifically as follows: Every preset time interval, the output power of each charging pile is redistributed using the allocated power; when a new electric vehicle comes to charge at any charging pile, the output power of each charging pile is also redistributed.

8. A power distribution system for new energy charging piles with multiple charging guns, comprising a memory, a processor, and a computer program stored in the memory and running on the processor, characterized in that, When the processor executes the computer program, it implements the steps of the method as described in any one of claims 1-7.

Images